ThinkTech AI SignalsThink Tech AI Strategy
The Think Tech AI Strategy provides a structured approach to trading by integrating liquidity-based entries, ATR volatility thresholds, and dynamic risk management. This strategy generates buy and sell signals while automatically calculating take profit and stop loss levels, boasting a 64% win rate based on historical data.
Usage
The strategy can be used to identify key breakout and retest opportunities. Liquidity-based zones act as potential accumulation and distribution areas and may serve as future support or resistance levels. Buy and sell zones are identified using liquidity zones and ATR-based filters. Risk management is built-in, automatically calculating take profit and stop loss levels using ATR multipliers. Volume and trend filtering options help confirm directional bias using a 50 EMA and RSI filter. The strategy also allows for session-based trading, limiting trades to key market hours for higher probability setups.
Settings
The risk/reward ratio can be adjusted to define the desired stop loss and take profit calculations. The ATR length and threshold determine ATR-based breakout conditions for dynamic entries. Liquidity period settings allow for customized analysis of price structure for support and resistance zones. Additional trend and RSI filters can be enabled to refine trade signals based on moving averages and momentum conditions. A session filter is included to restrict trade signals to specific market hours.
Style
The strategy includes options to display liquidity lines, showing key support and resistance areas. The first 15-minute candle breakout zones can also be visualized to highlight critical market structure points. A win/loss statistics table is included to track trade performance directly on the chart.
This strategy is intended for descriptive analysis and should be used alongside other confluence factors. Optimize your trading process with Think Tech AI today!
볼래틸리티
Trapped Traders Order BlocksHow It Works
The Trapped Traders Order Blocks indicator identifies specific price action patterns that suggest large market participants ("big money") have been trapped in losing positions after significant price sweeps, creating potential opportunities for reversals. The indicator detects both "bullish trap blocks" (where bearish traders are trapped) and "bearish trap blocks" (where bullish traders are trapped). Here’s the step-by-step process for each:
Bullish Trap Block (Bears Trapped):
A bearish candle (Candle A) must sweep the high of the previous candle (Candle B), meaning its high exceeds the high of the prior candle.
This bearish candle must have a longer upper wick than its lower wick, indicating rejection of higher prices.
The candle must not be a doji (i.e., it must have a significant body, defined as the body being at least 10% of the candle's range).
The next candle (Candle C) must close above the body of the bearish candle (Candle A), suggesting that price has immediately moved against the bearish sweep, potentially trapping bearish traders who entered short positions expecting a downward move.
The body of the bearish candle (Candle A) is marked as a "bullish trap block." A box is drawn around this candle's body, and a label ("Bullish Trap") is placed below it.
Bearish Trap Block (Bulls Trapped):
A bullish candle (Candle A) must sweep the low of the previous candle (Candle B), meaning its low is below the low of the prior candle.
This bullish candle must have a longer lower wick than its upper wick, indicating rejection of lower prices.
The candle must not be a doji.
The next candle (Candle C) must close below the body of the bullish candle (Candle A), suggesting that price has immediately moved against the bullish sweep, potentially trapping bullish traders who entered long positions expecting an upward move.
The body of the bullish candle (Candle A) is marked as a "bearish trap block." A box is drawn around this candle's body, and a label ("Bearish Trap") is placed above it.
Dynamic Box Extension:
For both bullish and bearish trap blocks, the box extends dynamically to the current bar unless it exceeds a user-defined age (default is 52 bars), at which point it stops at the maximum age.
Sweep Detection:
Bullish Sweep (of any trap block, bullish or bearish):
The current candle's open is above the top of the box.
The low is below the top of the box.
The close is above the top of the box.
The lower wick is longer than the upper wick (indicating rejection of lower prices).
The close is above 50% of the candle's range (ensuring a strong bullish bias).
When a bullish sweep occurs, a label ("Bullish Sweep") is placed at the low of the candle, pointing upward, and an alert is triggered.
Bearish Sweep (of any trap block, bullish or bearish):
The current candle's open is below the bottom of the box.
The high is above the bottom of the box.
The close is below the bottom of the box.
The upper wick is longer than the lower wick (indicating rejection of higher prices).
The close is below 50% of the candle's range (ensuring a strong bearish bias).
When a bearish sweep occurs, a label ("Bearish Sweep") is placed at the high of the candle, pointing downward, and an alert is triggered.
When to Be Used
The Trapped Traders Order Blocks indicator is best used in the following scenarios:
Reversal Trading:
Use this indicator to identify potential reversal points in the market. Bullish trap blocks suggest that trapped bears may unwind their short positions, leading to a potential bullish move. Bearish trap blocks suggest that trapped bulls may unwind their long positions, leading to a potential bearish move.
Look for sweeps of these blocks as confirmation of a directional move. A bullish sweep indicates a potential upward move, while a bearish sweep indicates a potential downward move.
Range-Bound Markets:
In sideways or ranging markets, trapped blocks can highlight key levels where large players have been caught off-guard. These levels often act as support or resistance, and a sweep of the block can signal a breakout or continuation in the direction of the sweep.
Confluence with Other Indicators:
Combine the trapped blocks with other technical analysis tools, such as support/resistance levels, Fibonacci retracements, or volume analysis, to increase the probability of a successful trade. For example, a bullish trap block near a strong support level with a bullish sweep can provide a high-probability setup for a long position, while a bearish trap block near a strong resistance level with a bearish sweep can signal a short opportunity.
Timeframes:
The indicator is most effective on higher timeframes such as 1-day (1D), 1-week (1W), and 1-month (1M) charts. These timeframes are more likely to capture significant moves involving large market participants, reducing noise and false signals compared to lower timeframes. While it can be used on lower timeframes (e.g., 1-hour or 4-hour), the signals may be less reliable due to increased market noise.
Logic Behind It
The logic behind the Trapped Traders Order Blocks indicator is rooted in market psychology and the behavior of large market participants ("big money"). When a large sweep candle occurs where price spikes in one direction but then quickly reverses it often indicates that traders have entered positions in the direction of the sweep, expecting a continuation. However, if the price immediately moves against them, these traders are now trapped in losing positions.
Bullish Trap Block (Bears Trapped):
A large bearish sweep candle (spiking upward but closing lower) suggests that bearish traders (bears) have entered short positions at the top of the move, expecting a downward continuation. If the next candle closes above the bearish candle's body, these bears are trapped in losing positions.
The body of the bearish candle becomes a "bullish trap block" because the trapped bears are likely to have placed their stop-loss orders or break-even exit orders just above the high of the sweep candle or within the body of the candle. As price revisits this level in the future, these trapped traders may attempt to unwind their positions by buying back their shorts, which can drive the price higher. This unwinding process often attracts new buyers, leading to a potential bullish reversal or continuation.
The bullish sweep conditions (e.g., close > box top, longer lower wick, and close above 50% of the range) ensure that the price action at the block level shows strong bullish momentum and rejection of lower prices, confirming the potential for a move higher.
Bearish Trap Block (Bulls Trapped):
A large bullish sweep candle (spiking downward but closing higher) suggests that bullish traders (bulls) have entered long positions at the bottom of the move, expecting an upward continuation. If the next candle closes below the bullish candle's body, these bulls are trapped in losing positions.
The body of the bullish candle becomes a "bearish trap block" because the trapped bulls are likely to have placed their stop-loss orders or break-even exit orders just below the low of the sweep candle or within the body of the candle. As price revisits this level in the future, these trapped traders may attempt to unwind their positions by selling their longs, which can drive the price lower. This unwinding process often attracts new sellers, leading to a potential bearish reversal or continuation.
The bearish sweep conditions (e.g., close < box bottom, longer upper wick, and close below 50% of the range) ensure that the price action at the block level shows strong bearish momentum and rejection of higher prices, confirming the potential for a move lower.
Summary
Bullish Trap Block: Occurs when bears get trapped after a bearish sweep candle is immediately followed by a bullish candle, indicating a potential reversal as trapped bears may unwind their positions.
Bearish Trap Block: Occurs when bulls get trapped after a bullish sweep candle is immediately followed by a bearish candle, indicating a potential bearish reversal.
Use Case: Ideal for identifying reversal opportunities, especially in range-bound markets or at key support/resistance levels on higher timeframes like 1D, 1W, and 1M, and can be combined with other indicators for confluence.
Logic: Large sweep candles followed by an immediate reversal suggest that big money has been trapped, and these traders may unwind their positions at break-even in the near future, driving price in the opposite direction of their initial trade.
This indicator provides a visual and actionable way to identify these trapped trader scenarios, with customizable settings for box display, sweep visuals, and alerts to help traders capitalize on these opportunities, particularly on higher timeframes where the signals are most reliable.
Hourly Volatility Explorer📊 Hourly Volatility Explorer: Master The Market's Pulse
Unlock the hidden rhythms of price action with this sophisticated volatility analysis tool. The Hourly Volatility Explorer reveals the most potent trading hours across multiple time zones, giving you a strategic edge in timing your trades.
🌟 Key Features:
⏰ Multi-Timezone Analysis
• GMT (UTC+0)
• EST (UTC-5) - New York
• BST (UTC+1) - London
• JST (UTC+9) - Tokyo
• AEST (UTC+10) - Sydney
Perfect for tracking major market sessions and their overlaps!
📈 Dynamic Visualization
• Color-gradient hourly bars for instant pattern recognition
• Real-time volatility comparison
• Interactive data table with comprehensive statistics
• Automatic highlighting of peak volatility periods
🎯 Strategic Applications:
Day Trading:
• Identify optimal trading windows
• Avoid low-liquidity periods
• Capitalize on session overlaps
• Fine-tune entry/exit timing
Risk Management:
• Set appropriate stop losses based on hourly volatility
• Adjust position sizes for different market hours
• Optimize risk-reward ratios
• Plan around high-impact hours
Global Market Analysis:
• Track volatility across all major sessions
• Spot institutional trading patterns
• Identify quiet vs. active periods
• Monitor 24/7 market dynamics
💡 Perfect For:
• Forex traders navigating global sessions
• Crypto traders in 24/7 markets
• Day traders optimizing execution times
• Algorithmic traders fine-tuning strategies
• Risk managers calibrating exposure
📊 Advanced Features:
• Rolling 3-month analysis for reliable patterns
• Precise pip movement calculations
• Sample size tracking for statistical validity
• Real-time current hour comparison
• Color-coded visual system for instant insights
⚡ Pro Trading Tips:
• Use during major session overlaps for maximum opportunity
• Compare patterns across different instruments
• Combine with volume analysis for deeper insights
• Track seasonal variations in hourly patterns
• Build trading schedules around peak hours
🎓 Educational Value:
• Understand market microstructure
• Learn global market dynamics
• Master timezone relationships
• Develop timing intuition
🛠️ Customization:
• Adjustable lookback period
• Flexible pip multiplier
• Multiple timezone options
• Visual preference settings
Whether you're scalping the 1-minute chart or managing longer-term positions, the Hourly Volatility Explorer provides the precise timing intelligence needed for today's global markets.
Transform your trading schedule from guesswork to science. Know exactly when markets move, why they move, and how to position yourself for maximum opportunity.
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Percentage ChangeThis is a very simple script. It calculations the percentage the stocks price has changed with 3 different metrics over the configured lookback period.
- Close to Open: calculates the percentage difference between the opening bar at the beginning of the lookback period to the closing current bar.
- Low to High: calculates the percentage difference between the lowest low to the highest high over the period.
- High to Low: calculates the percentage difference between the highest high to the lowest low over the period.
This indicator is used to call out especially volatile periods allowing traders to target spikes up or down.
Nef33 Forex & Crypto Trading Signals PRO
1. Understanding the Indicator's Context
The indicator generates signals based on confluence (trend, volume, key zones, etc.), but it does not include predefined SL or TP levels. To establish them, we must:
Use dynamic or static support/resistance levels already present in the script.
Incorporate volatility (such as ATR) to adjust the levels based on market conditions.
Define a risk/reward ratio (e.g., 1:2).
2. Options for Determining SL and TP
Below, I provide several ideas based on the tools available in the script:
Stop Loss (SL)
The SL should protect you from adverse movements. You can base it on:
ATR (Volatility): Use the smoothed ATR (atr_smooth) multiplied by a factor (e.g., 1.5 or 2) to set a dynamic SL.
Buy: SL = Entry Price - (atr_smooth * atr_mult).
Sell: SL = Entry Price + (atr_smooth * atr_mult).
Key Zones: Place the SL below a support (for buys) or above a resistance (for sells), using Order Blocks, Fair Value Gaps, or Liquidity Zones.
Buy: SL below the nearest ob_lows or fvg_lows.
Sell: SL above the nearest ob_highs or fvg_highs.
VWAP: Use the daily VWAP (vwap_day) as a critical level.
Buy: SL below vwap_day.
Sell: SL above vwap_day.
Take Profit (TP)
The TP should maximize profits. You can base it on:
Risk/Reward Ratio: Multiply the SL distance by a factor (e.g., 2 or 3).
Buy: TP = Entry Price + (SL Distance * 2).
Sell: TP = Entry Price - (SL Distance * 2).
Key Zones: Target the next resistance (for buys) or support (for sells).
Buy: TP at the next ob_highs, fvg_highs, or liq_zone_high.
Sell: TP at the next ob_lows, fvg_lows, or liq_zone_low.
Ichimoku: Use the cloud levels (Senkou Span A/B) as targets.
Buy: TP at senkou_span_a or senkou_span_b (whichever is higher).
Sell: TP at senkou_span_a or senkou_span_b (whichever is lower).
3. Practical Implementation
Since the script does not automatically draw SL/TP, you can:
Calculate them manually: Observe the chart and use the levels mentioned.
Modify the code: Add SL/TP as labels (label.new) at the moment of the signal.
Here’s an example of how to modify the code to display SL and TP based on ATR with a 1:2 risk/reward ratio:
Modified Code (Signals Section)
Find the lines where the signals (trade_buy and trade_sell) are generated and add the following:
pinescript
// Calculate SL and TP based on ATR
atr_sl_mult = 1.5 // Multiplier for SL
atr_tp_mult = 3.0 // Multiplier for TP (1:2 ratio)
sl_distance = atr_smooth * atr_sl_mult
tp_distance = atr_smooth * atr_tp_mult
if trade_buy
entry_price = close
sl_price = entry_price - sl_distance
tp_price = entry_price + tp_distance
label.new(bar_index, low, "Buy: " + str.tostring(math.round(bull_conditions, 1)), color=color.green, textcolor=color.white, style=label.style_label_up, size=size.tiny)
label.new(bar_index, sl_price, "SL: " + str.tostring(math.round(sl_price, 2)), color=color.red, textcolor=color.white, style=label.style_label_down, size=size.tiny)
label.new(bar_index, tp_price, "TP: " + str.tostring(math.round(tp_price, 2)), color=color.blue, textcolor=color.white, style=label.style_label_up, size=size.tiny)
if trade_sell
entry_price = close
sl_price = entry_price + sl_distance
tp_price = entry_price - tp_distance
label.new(bar_index, high, "Sell: " + str.tostring(math.round(bear_conditions, 1)), color=color.red, textcolor=color.white, style=label.style_label_down, size=size.tiny)
label.new(bar_index, sl_price, "SL: " + str.tostring(math.round(sl_price, 2)), color=color.red, textcolor=color.white, style=label.style_label_up, size=size.tiny)
label.new(bar_index, tp_price, "TP: " + str.tostring(math.round(tp_price, 2)), color=color.blue, textcolor=color.white, style=label.style_label_down, size=size.tiny)
Code Explanation
SL: Calculated by subtracting/adding sl_distance to the entry price (close) depending on whether it’s a buy or sell.
TP: Calculated with a double distance (tp_distance) for a 1:2 risk/reward ratio.
Visualization: Labels are added to the chart to display SL (red) and TP (blue).
4. Practical Strategy Without Modifying the Code
If you don’t want to modify the script, follow these steps manually:
Entry: Take the trade_buy or trade_sell signal.
SL: Check the smoothed ATR (atr_smooth) on the chart or calculate a fixed level (e.g., 1.5 times the ATR). Also, review nearby key zones (OB, FVG, VWAP).
TP: Define a target based on the next key zone or multiply the SL distance by 2 or 3.
Example:
Buy at 100, ATR = 2.
SL = 100 - (2 * 1.5) = 97.
TP = 100 + (2 * 3) = 106.
5. Recommendations
Test in Demo: Apply this logic in a demo account to adjust the multipliers (atr_sl_mult, atr_tp_mult) based on the market (forex or crypto).
Combine with Zones: If the ATR-based SL is too wide, use the nearest OB or FVG as a reference.
Risk/Reward Ratio: Adjust the TP based on your tolerance (1:1, 1:2, 1:3)
iD EMARSI on ChartSCRIPT OVERVIEW
The EMARSI indicator is an advanced technical analysis tool that maps RSI values directly onto price charts. With adaptive scaling capabilities, it provides a unique visualization of momentum that flows naturally with price action, making it particularly valuable for FOREX and low-priced securities trading.
KEY FEATURES
1 PRICE MAPPED RSI VISUALIZATION
Unlike traditional RSI that displays in a separate window, EMARSI plots the RSI directly on the price chart, creating a flowing line that identifies momentum shifts within the context of price action:
// Map RSI to price chart with better scaling
mappedRsi = useAdaptiveScaling ?
median + ((rsi - 50) / 50 * (pQH - pQL) / 2 * math.min(1.0, 1/scalingFactor)) :
down == pQL ? pQH : up == pQL ? pQL : median - (median / (1 + up / down))
2 ADAPTIVE SCALING SYSTEM
The script features an intelligent scaling system that automatically adjusts to different market conditions and price levels:
// Calculate adaptive scaling factor based on selected method
scalingFactor = if scalingMethod == "ATR-Based"
math.min(maxScalingFactor, math.max(1.0, minTickSize / (atrValue/avgPrice)))
else if scalingMethod == "Price-Based"
math.min(maxScalingFactor, math.max(1.0, math.sqrt(100 / math.max(avgPrice, 0.01))))
else // Volume-Based
math.min(maxScalingFactor, math.max(1.0, math.sqrt(1000000 / math.max(volume, 100))))
3 MODIFIED RSI CALCULATION
EMARSI uses a specially formulated RSI calculation that works with an adaptive base value to maintain consistency across different price ranges:
// Adaptive RSI Base based on price levels to improve flow
adaptiveRsiBase = useAdaptiveScaling ? rsiBase * scalingFactor : rsiBase
// Calculate RSI components with adaptivity
up = ta.rma(math.max(ta.change(rsiSourceInput), adaptiveRsiBase), emaSlowLength)
down = ta.rma(-math.min(ta.change(rsiSourceInput), adaptiveRsiBase), rsiLengthInput)
// Improved RSI calculation with value constraint
rsi = down == 0 ? 100 : up == 0 ? 0 : 100 - (100 / (1 + up / down))
4 MOVING AVERAGE CROSSOVER SYSTEM
The indicator creates a smooth moving average of the RSI line, enabling a crossover system that generates trading signals:
// Calculate MA of mapped RSI
rsiMA = ma(mappedRsi, emaSlowLength, maTypeInput)
// Strategy entries
if ta.crossover(mappedRsi, rsiMA)
strategy.entry("RSI Long", strategy.long)
if ta.crossunder(mappedRsi, rsiMA)
strategy.entry("RSI Short", strategy.short)
5 VISUAL REFERENCE FRAMEWORK
The script includes visual guides that help interpret the RSI movement within the context of recent price action:
// Calculate pivot high and low
pQH = ta.highest(high, hlLen)
pQL = ta.lowest(low, hlLen)
median = (pQH + pQL) / 2
// Plotting
plot(pQH, "Pivot High", color=color.rgb(82, 228, 102, 90))
plot(pQL, "Pivot Low", color=color.rgb(231, 65, 65, 90))
med = plot(median, style=plot.style_steplinebr, linewidth=1, color=color.rgb(238, 101, 59, 90))
6 DYNAMIC COLOR SYSTEM
The indicator uses color fills to clearly visualize the relationship between the RSI and its moving average:
// Color fills based on RSI vs MA
colUp = mappedRsi > rsiMA ? input.color(color.rgb(128, 255, 0), '', group= 'RSI > EMA', inline= 'up') :
input.color(color.rgb(240, 9, 9, 95), '', group= 'RSI < EMA', inline= 'dn')
colDn = mappedRsi > rsiMA ? input.color(color.rgb(0, 230, 35, 95), '', group= 'RSI > EMA', inline= 'up') :
input.color(color.rgb(255, 47, 0), '', group= 'RSI < EMA', inline= 'dn')
fill(rsiPlot, emarsi, mappedRsi > rsiMA ? pQH : rsiMA, mappedRsi > rsiMA ? rsiMA : pQL, colUp, colDn)
7 REAL TIME PARAMETER MONITORING
A transparent information panel provides real-time feedback on the adaptive parameters being applied:
// Information display
var table infoPanel = table.new(position.top_right, 2, 3, bgcolor=color.rgb(0, 0, 0, 80))
if barstate.islast
table.cell(infoPanel, 0, 0, "Current Scaling Factor", text_color=color.white)
table.cell(infoPanel, 1, 0, str.tostring(scalingFactor, "#.###"), text_color=color.white)
table.cell(infoPanel, 0, 1, "Adaptive RSI Base", text_color=color.white)
table.cell(infoPanel, 1, 1, str.tostring(adaptiveRsiBase, "#.####"), text_color=color.white)
BENEFITS FOR TRADERS
INTUITIVE MOMENTUM VISUALIZATION
By mapping RSI directly onto the price chart, traders can immediately see the relationship between momentum and price without switching between different indicator windows.
ADAPTIVE TO ANY MARKET CONDITION
The three scaling methods (ATR-Based, Price-Based, and Volume-Based) ensure the indicator performs consistently across different market conditions, volatility regimes, and price levels.
PREVENTS EXTREME VALUES
The adaptive scaling system prevents the RSI from generating extreme values that exceed chart boundaries when trading low-priced securities or during high volatility periods.
CLEAR TRADING SIGNALS
The RSI and moving average crossover system provides clear entry signals that are visually reinforced through color changes, making it easy to identify potential trading opportunities.
SUITABLE FOR MULTIPLE TIMEFRAMES
The indicator works effectively across multiple timeframes, from intraday to daily charts, making it versatile for different trading styles and strategies.
TRANSPARENT PARAMETER ADJUSTMENT
The information panel provides real-time feedback on how the adaptive system is adjusting to current market conditions, helping traders understand why the indicator is behaving as it is.
CUSTOMIZABLE VISUALIZATION
Multiple visualization options including Bollinger Bands, different moving average types, and customizable colors allow traders to adapt the indicator to their personal preferences.
CONCLUSION
The EMARSI indicator represents a significant advancement in RSI visualization by directly mapping momentum onto price charts with adaptive scaling. This approach makes momentum shifts more intuitive to identify and helps prevent the scaling issues that commonly affect RSI-based indicators when applied to low-priced securities or volatile markets.
Deviation ChannelsIndicator Name: Deviation Channels (Dev Chan)
Why Use This Indicator?
Visualize Volatility Ranges:
The indicator plots Keltner Channels at four levels above and below an average line, letting you easily see how far price has deviated from a typical range. Each “dev” line highlights potential support or resistance during pullbacks or surges.
Color-Coded Clarity:
Each band shifts color intensity depending on whether the current price is trading above or below it, letting you spot breakouts and rejections at a glance. Meanwhile, the Fast SMA (default 10) also changes color – green if price is above, red if below – adding a quick momentum read.
Adjustable Source & Length:
Choose your input source (open, close, ohlc4, or hlc3) and set your Keltner length to suit different asset classes or timeframes. Whether you want a tighter, more reactive channel or a smoother, longer-term reading, the script adapts with minimal effort.
A Simple Trading Approach
Identify Trend with Fast SMA:
If the Fast SMA (default length 10) is green (price above it), treat that as a bullish environment. If it’s red (price below), favor bearish or neutral stances.
Wait for Price to Reach Lower/Upper Deviations:
In a bullish setup (Fast SMA green), watch for price to dip into one of the lower channels (e.g., -1 Dev or -2 Dev). Such pullbacks can become potential “buy the dip” zones if price stabilizes and resumes upward momentum.
Conversely, if the Fast SMA is red, watch for price to test the upper channels (1 Dev or 2 Dev). That might be a short opportunity or a place to close out any remaining longs before a deeper correction.
Manage Risk with Channel Levels:
Place stop-losses just beyond the next “dev” band to protect against volatility. For example, if you enter on a bounce at -1 Dev, consider placing a stop near -2 Dev or -3 Dev, depending on your risk tolerance.
Take Profits Gradually:
In an uptrend, you might scale out of positions as price moves toward higher lines (e.g., 1 Dev or 2 Dev). Conversely, if price fails to hold above the Fast SMA or repeatedly closes below a key band, it might be time to exit.
Disclaimer: No single indicator is foolproof. Always combine with sound risk management, observe multiple timeframes, and consider fundamental factors before making trading decisions. Experiment with the Keltner length and Fast SMA fastLength to find the sweet spot for your market and time horizon.
Adaptive Bollinger BandsAdaptive Bollinger Bands
This indicator displays Bollinger Bands with parameters that dynamically adjust based on market volatility. Unlike standard Bollinger Bands with fixed parameters, this version adaptively modifies both the period and standard deviation multiplier in real-time based on measured market conditions.
Key Features
Dynamic adjustment of period and standard deviation based on normalized volatility
Color-coded visualization of current volatility regime (expanding, normal, contracting)
Integration with Keltner Channels for band refinement
Bandwidth analysis for volatility regime identification
Optional on-chart parameter labels showing current settings
Band cross alerts and visual markers
Volatility Visualization
The indicator uses color-coding to display different volatility regimes:
Red: Expanding volatility regime (higher measured volatility)
Blue: Normal volatility regime (average measurements)
Green: Contracting volatility regime (lower measured volatility)
Technical Information
The indicator calculates volatility by analyzing price returns over a configurable lookback period (default 50 bars). The standard deviation of returns is normalized against historical extremes to create an adaptive scaling factor.
Band adaptation occurs through two primary mechanisms:
1. Period adjustment: Higher volatility uses shorter periods (more responsive), while lower volatility uses longer periods (more stable)
2. Standard deviation multiplier adjustment: Higher volatility increases the multiplier (wider bands), while lower volatility decreases it (tighter bands)
The middle band uses a simple moving average with the adaptive period. Additional refinement occurs through Keltner Channel integration, which can tighten bands when contained within Keltner boundaries.
Volatility regimes are determined by analyzing Bollinger Bandwidth relative to its recent history, providing contextual information about the current market state.
Settings Customization
The indicator provides extensive customization options:
- Base parameters (period and standard deviation)
- Adaptive range limits (min/max period and standard deviation)
- Keltner Channel parameters for band refinement
- Bandwidth analysis settings
- Display options for visual elements
Limitations and Considerations
All technical indicators have inherent limitations and should not be used in isolation
Past performance does not guarantee future results
The indicator requires sufficient historical data for proper volatility normalization
Smaller timeframes may produce more noise in the adaptive calculations
Parameters may require adjustment for different markets and trading styles
Band crosses are not trading signals on their own and should be evaluated with other factors
This indicator is designed to provide objective information about market volatility conditions and potential support/resistance zones. Always combine with other analysis methods within a comprehensive trading approach.
FlexATRFlexATR: A Dynamic Multi-Timeframe Trading Strategy
Overview: FlexATR is a versatile trading strategy that dynamically adapts its key parameters based on the timeframe being used. It combines technical signals from exponential moving averages (EMAs) and the Relative Strength Index (RSI) with volatility-based risk management via the Average True Range (ATR). This approach helps filter out false signals while adjusting to varying market conditions — whether you’re trading on a daily chart, intraday charts (30m, 60m, or 5m), or even on higher timeframes like the 4-hour or weekly charts.
How It Works:
Multi-Timeframe Parameter Adaptation: FlexATR is designed to automatically adjust its indicator settings depending on the timeframe:
Daily and Weekly: On higher timeframes, the strategy uses longer periods for the fast and slow EMAs and standard periods for RSI and ATR to capture more meaningful trend confirmations while minimizing noise.
Intraday (e.g., 30m, 60m, 5m, 4h): The parameters are converted from “days” into the corresponding number of bars. For instance, on a 30-minute chart, a “day” might equal 48 bars. The preset values for a 30-minute chart have been slightly reduced (e.g., a fast EMA is set at 0.35 days instead of 0.4) to improve reactivity while maintaining robust filtering.
Signal Generation:
Entry Signals: The strategy enters long positions when the fast EMA crosses above the slow EMA and the RSI is above 50, and it enters short positions when the fast EMA crosses below the slow EMA with the RSI below 50. This dual confirmation helps ensure that signals are reliable.
Risk Management: The ATR is used to compute dynamic levels for stop loss and profit target:
Stop Loss: For a long position, the stop loss is placed at Price - (ATR × Stop Loss Multiplier). For a short position, it is at Price + (ATR × Stop Loss Multiplier).
Profit Target: The profit target is similarly set using the ATR multiplied by a designated profit multiplier.
Dynamic Trailing Stop: FlexATR further incorporates a dynamic trailing stop (if enabled) that adjusts according to the ATR. This trailing stop follows favorable price movements at a distance defined by a multiplier, locking in gains as the trend develops. The use of a trailing stop helps protect profits without requiring a fixed exit point.
Capital Allocation: Each trade is sized at 10% of the total equity. This percentage-based position sizing allows the strategy to scale with your account size. While the current setup assumes no leverage (a 1:1 exposure), the inherent design of the strategy means you can adjust the leverage externally if desired, with risk metrics scaling accordingly.
Visual Representation: For clarity and accessibility (especially for those with color vision deficiencies), FlexATR employs a color-blind friendly palette (the Okabe-Ito palette):
EMA Fast: Displayed in blue.
EMA Slow: Displayed in orange.
Stop Loss Levels: Rendered in vermilion.
Profit Target Levels: Shown in a distinct azzurro (light blue).
Benefits and Considerations:
Reliability: By requiring both EMA crossovers and an RSI confirmation, FlexATR filters out a significant amount of market noise, which reduces false signals at the expense of some delayed entries.
Adaptability: The automatic conversion of “day-based” parameters into bar counts for intraday charts means the strategy remains consistent across different timeframes.
Risk Management: Using the ATR for both fixed and trailing stops allows the strategy to adapt to changing market volatility, helping to protect your capital.
Flexibility: The strategy’s inputs are customizable via the input panel, allowing traders to fine-tune the parameters for different assets or market conditions.
Conclusion: FlexATR is designed as a balanced, adaptive strategy that emphasizes reliability and robust risk management across a variety of timeframes. While it may sometimes enter trades slightly later due to its filtering mechanism, its focus on confirming trends helps reduce the likelihood of false signals. This makes it particularly attractive for traders who prioritize a disciplined, multi-timeframe approach to capturing market trends.
Elastic Volume-Weighted Student-T TensionOverview
The Elastic Volume-Weighted Student-T Tension Bands indicator dynamically adapts to market conditions using an advanced statistical model based on the Student-T distribution. Unlike traditional Bollinger Bands or Keltner Channels, this indicator leverages elastic volume-weighted averaging to compute real-time dispersion and location parameters, making it highly responsive to volatility changes while maintaining robustness against price fluctuations.
This methodology is inspired by incremental calculation techniques for weighted mean and variance, as outlined in the paper by Tony Finch:
📄 "Incremental Calculation of Weighted Mean and Variance" .
Key Features
✅ Adaptive Volatility Estimation – Uses an exponentially weighted Student-T model to dynamically adjust band width.
✅ Volume-Weighted Mean & Dispersion – Incorporates real-time volume weighting, ensuring a more accurate representation of market sentiment.
✅ High-Timeframe Volume Normalization – Provides an option to smooth volume impact by referencing a higher timeframe’s cumulative volume, reducing noise from high-variability bars.
✅ Customizable Tension Parameters – Configurable standard deviation multipliers (σ) allow for fine-tuned volatility sensitivity.
✅ %B-Like Oscillator for Relative Price Positioning – The main indicator is in form of a dedicated oscillator pane that normalizes price position within the sigma ranges, helping identify overbought/oversold conditions and potential momentum shifts.
✅ Robust Statistical Foundation – Utilizes kurtosis-based degree-of-freedom estimation, enhancing responsiveness across different market conditions.
How It Works
Volume-Weighted Elastic Mean (eμ) – Computes a dynamic mean price using an elastic weighted moving average approach, influenced by trade volume, if not volume detected in series, study takes true range as replacement.
Dispersion (eσ) via Student-T Distribution – Instead of assuming a fixed normal distribution, the bands adapt to heavy-tailed distributions using kurtosis-driven degrees of freedom.
Incremental Calculation of Variance – The indicator applies Tony Finch’s incremental method for computing weighted variance instead of arithmetic sum's of fixed bar window or arrays, improving efficiency and numerical stability.
Tension Calculation – There are 2 dispersion custom "zones" that are computed based on the weighted mean and dynamically adjusted standard student-t deviation.
%B-Like Oscillator Calculation – The oscillator normalizes the price within the band structure, with values between 0 and 1:
* 0.00 → Price is at the lower band (-2σ).
* 0.50 → Price is at the volume-weighted mean (eμ).
* 1.00 → Price is at the upper band (+2σ).
* Readings above 1.00 or below 0.00 suggest extreme movements or possible breakouts.
Recommended Usage
For scalping in lower timeframes, it is recommended to use the fixed α Decay Factor, it is in raw format for better control, but you can easily make a like of transformation to N-bar size window like in EMA-1 bar dividing 2 / decayFactor or like an RMA dividing 1 / decayFactor.
The HTF selector catch quite well Higher Time Frame analysis, for example using a Daily chart and using as HTF the 200-day timeframe, weekly or monthly.
Suitable for trend confirmation, breakout detection, and mean reversion plays.
The %B-like oscillator helps gauge momentum strength and detect divergences in price action if user prefer a clean chart without bands, this thanks to pineScript v6 force overlay feature.
Ideal for markets with volume-driven momentum shifts (e.g., futures, forex, crypto).
Customization Parameters
Fixed α Decay Factor – Controls the rate of volume weighting influence for an approximation EWMA approach instead of using sum of series or arrays, making the code lightweight & computing fast O(1).
HTF Volume Smoothing – Instead of a fixed denominator for computing α , a volume sum of the last 2 higher timeframe closed candles are used as denominator for our α weight factor. This is useful to review mayor trends like in daily, weekly, monthly.
Tension Multipliers (±σ) – Adjusts sensitivity to dispersion sigma parameter (volatility).
Oscillator Zone Fills – Visual cues for price positioning within the cloud range.
Posible Interpretations
As market within indicators relay on each individual edge, this are just some key ideas to glimpse how the indicator could be interpreted by the user:
📌 Price inside bands – Market is considered somehow "stable"; price is like resting from tension or "charging batteries" for volume spike moves.
📌 Price breaking outer bands – Potential breakout or extreme movement; watch for reversals or continuation from strong moves. Market is already in tension or generating it.
📌 Narrowing Bands – Decreasing volatility; expect contraction before expansion.
📌 Widening Bands – Increased volatility; prepare for high probability pull-back moves, specially to the center location of the bands (the mean) or the other side of them.
📌 Oscillator is just the interpretation of the price normalized across the Student-T distribution fitting "curve" using the location parameter, our Elastic Volume weighted mean (eμ) fixed at 0.5 value.
Final Thoughts
The Elastic Volume-Weighted Student-T Tension indicator provides a powerful, volume-sensitive alternative to traditional volatility bands. By integrating real-time volume analysis with an adaptive statistical model, incremental variance computation, in a relative price oscillator that can be overlayed in the chart as bands, it offers traders an edge in identifying momentum shifts, trend strength, and breakout potential. Think of the distribution as a relative "tension" rubber band in which price never leave so far alone.
DISCLAIMER:
The Following indicator/code IS NOT intended to be a formal investment advice or recommendation by the author, nor should be construed as such. Users will be fully responsible by their use regarding their own trading vehicles/assets.
The following indicator was made for NON LUCRATIVE ACTIVITIES and must remain as is, following TradingView's regulations. Use of indicator and their code are published for work and knowledge sharing. All access granted over it, their use, copy or re-use should mention authorship(s) and origin(s).
WARNING NOTICE!
THE INCLUDED FUNCTION MUST BE CONSIDERED FOR TESTING. The models included in the indicator have been taken from open sources on the web and some of them has been modified by the author, problems could occur at diverse data sceneries, compiler version, or any other externality.
ETH/USDT EMA Crossover Strategy - OptimizedStrategy Name: EMA Crossover Strategy for ETH/USDT
Description:
This trading strategy is designed for the ETH/USDT pair and is based on exponential moving average (EMA) crossovers combined with momentum and volatility indicators. The strategy uses multiple filters to identify high-probability signals in both bullish and bearish trends, making it suitable for traders looking to trade in trending markets.
Strategy Components
EMAs (Exponential Moving Averages):
EMA 200: Used to identify the primary trend. If the price is above the EMA 200, it is considered a bullish trend; if below, a bearish trend.
EMA 50: Acts as an additional filter to confirm the trend.
EMA 20 and EMA 50 Short: These short-term EMAs generate entry signals through crossovers. A bullish crossover (EMA 20 crosses above EMA 50 Short) is a buy signal, while a bearish crossover (EMA 20 crosses below EMA 50 Short) is a sell signal.
RSI (Relative Strength Index):
The RSI is used to avoid overbought or oversold conditions. Long trades are only taken when the RSI is above 30, and short trades when the RSI is below 70.
ATR (Average True Range):
The ATR is used as a volatility filter. Trades are only taken when there is sufficient volatility, helping to avoid false signals in quiet markets.
Volume:
A volume filter is used to confirm sufficient market participation in the price movement. Trades are only taken when volume is above average.
Strategy Logic
Long Trades:
The price must be above the EMA 200 (bullish trend).
The EMA 20 must cross above the EMA 50 Short.
The RSI must be above 30.
The ATR must indicate sufficient volatility.
Volume must be above average.
Short Trades:
The price must be below the EMA 200 (bearish trend).
The EMA 20 must cross below the EMA 50 Short.
The RSI must be below 70.
The ATR must indicate sufficient volatility.
Volume must be above average.
How to Use the Strategy
Setup:
Add the script to your ETH/USDT chart on TradingView.
Adjust the parameters according to your preferences (e.g., EMA periods, RSI, ATR, etc.).
Signals:
Buy and sell signals will be displayed directly on the chart.
Long trades are indicated with an upward arrow, and short trades with a downward arrow.
Risk Management:
Use stop-loss and take-profit orders in all trades.
Consider a risk-reward ratio of at least 1:2.
Backtesting:
Test the strategy on historical data to evaluate its performance before using it live.
Advantages of the Strategy
Trend-focused: The strategy is designed to trade in trending markets, increasing the probability of success.
Multiple filters: The use of RSI, ATR, and volume reduces false signals.
Adaptability: It can be adjusted for different timeframes, although it is recommended to test it on 5-minute and 15-minute charts for ETH/USDT.
Warnings
Sideways markets: The strategy may generate false signals in markets without a clear trend. It is recommended to avoid trading in such conditions.
Optimization: Make sure to optimize the parameters according to the market and timeframe you are using.
Risk management: Never trade without stop-loss and take-profit orders.
Author
Jose J. Sanchez Cuevas
Version
v1.0
MLB Momentum IndicatorMLB Momentum Indicator is a no‐lookahead technical indicator designed to signal intraday trend shifts and potential reversal points. It combines several well‐known technical components—Moving Averages, MACD, RSI, and optional ADX & Volume filters—to deliver high‐probability buy/sell signals on your chart.
Below is an overview of how it works and what each part does:
1. Moving Average Trend Filter
The script uses two moving averages (fast and slow) to determine the primary trend:
isUpTrend if Fast MA > Slow MA
isDownTrend if Fast MA < Slow MA
You can select the MA method—SMA, EMA, or WMA—and customize lengths.
Why it matters: The indicator only gives bullish signals if the trend is up, and bearish signals if the trend is down, helping avoid trades that go against the bigger flow.
2. MACD Confirmation (Momentum)
Uses MACD (with user‐defined Fast, Slow, and Signal lengths) to check momentum:
macdBuySignal if the MACD line crosses above its signal line (bullish)
macdSellSignal if the MACD line crosses below its signal line (bearish)
Why it matters: MACD crossovers confirm an emerging momentum shift, aligning signals with actual price acceleration rather than random fluctuation.
3. RSI Overbought/Oversold Filter
RSI (Relative Strength Index) is calculated with a chosen length, plus Overbought & Oversold thresholds:
For long signals: the RSI must be below the Overbought threshold (e.g. 70).
For short signals: the RSI must be above the Oversold threshold (e.g. 30).
Why it matters: Prevents buying when price is already overbought or shorting when price is too oversold, filtering out possible poor‐risk trades.
4. Optional ADX Filter (Trend Strength)
If enabled, ADX must exceed a chosen threshold (e.g., 20) for a signal to be valid:
This ensures you’re only taking trades in markets that have sufficient directional momentum.
Why it matters: It weeds out choppy, sideways conditions where signals are unreliable.
5. Optional Volume Filter (High‐Participation Moves)
If enabled, the indicator checks whether current volume is above a certain multiple of its moving average (e.g., 1.5× average volume).
Why it matters: High volume often indicates stronger institutional interest, validating potential breakouts or reversals.
6. ATR & Chandelier (Visual Reference)
For reference only, the script can display ATR‐based stop levels or a Chandelier Exit line:
ATR (Average True Range) helps gauge volatility and can inform stop‐loss distances.
Chandelier Exit is a trailing stop technique that adjusts automatically as price moves.
Why it matters: Though this version of the script doesn’t execute trades, these lines help you see how far to place stops or how to ride a trend.
7. Final Bullish / Bearish Signal
When all conditions (trend, MACD, RSI, optional ADX, optional Volume) line up for a long, a green “Long” arrow appears.
When all conditions line up for a short, a red “Short” arrow appears.
Why it matters: You get a clear, on‐chart signal for each potential entry, rather than needing to check multiple indicators manually.
8. Session & Date Filtering
The script allows choosing a start/end date and an optional session window (e.g. 09:30–16:00).
Why it matters: Helps limit signals to a specific historical backtest range or trading hours, which can be crucial for day traders (e.g., stock market hours only).
Putting It All Together
Primary Trend → ensures you trade in line with the bigger direction.
MACD & RSI → confirm momentum and avoid overbought/oversold extremes.
ADX & Volume → optional filters for strong trend strength & genuine interest.
Arrows → each potential buy (Long) or sell (Short) signal is clearly shown on your chart.
Use Cases
5‐Minute Scalping: Shorter RSI/MACD lengths to catch small, frequent intraday moves.
Swing Trading: Larger MAs, bigger RSI thresholds, and using ADX to filter only major trends.
Cautious Approach: Enable volume & ADX filters to reduce false signals in choppy markets.
Benefits & Limitations
Benefits:
Consolidates multiple indicators into one overlay.
Clear buy/sell signals with optional dynamic volatility references.
Flexible user inputs adapt to different trading styles/timeframes.
Limitations:
Like all technical indicators, it can produce false signals in sideways or news‐driven markets.
Success depends heavily on user settings and the particular market’s behavior.
Summary
The MLB Momentum Indicator combines a trend filter (MAs), momentum check (MACD), overbought/oversold gating (RSI), and optional ADX/Volume filters to create clear buy/sell arrows on your chart. This approach encourages trading in sync with both trend and momentum, and helps avoid suboptimal entries when volume or trend strength is lacking. It can be tailored to scalp micro‐moves on lower timeframes or used for higher‐timeframe swing trading by adjusting the input settings.
Uptrick: Acceleration ShiftsIntroduction
Uptrick: Acceleration Shifts is designed to measure and visualize price momentum shifts by focusing on acceleration —the rate of change in velocity over time. It uses various moving average techniques as a trend filter, providing traders with a clearer perspective on market direction and potential trade entries or exits.
Purpose
The main goal of this indicator is to spot strong momentum changes (accelerations) and confirm them with a chosen trend filter. It attempts to distinguish genuine market moves from noise, helping traders make more informed decisions. The script can also trigger multiple entries (smart pyramiding) within the same trend, if desired.
Overview
By measuring how quickly price velocity changes (acceleration) and comparing it against a smoothed average of itself, this script generates buy or sell signals once the acceleration surpasses a given threshold. A trend filter is added for further validation. Users can choose from multiple smoothing methods and color schemes, and they can optionally enable a small table that displays real-time acceleration values.
Originality and Uniqueness
This script offers an acceleration-based approach, backed by several different moving average choices. The blend of acceleration thresholds, a trend filter, and an optional extra-entry (pyramiding) feature provides a flexible toolkit for various trading styles. The inclusion of multiple color themes and a slope-based coloring of the trend line adds clarity and user customization.
Inputs & Features
1. Acceleration Length (length)
This input determines the number of bars used when calculating velocity. Specifically, the script computes velocity by taking the difference in closing prices over length bars, and then calculates acceleration based on how that velocity changes over an additional length. The default is 14.
2. Trend Filter Length (smoothing)
This sets the lookback period for the chosen trend filter method. The default of 50 results in a moderately smooth trend line. A higher smoothing value will create a slower-moving trend filter.
3. Acceleration Threshold (threshold)
This multiplier determines when acceleration is considered strong enough to trigger a main buy or sell signal. A default value of 2.5 means the current acceleration must exceed 2.5 times the average acceleration before signaling.
4. Smart Pyramiding Strength (pyramidingThreshold)
This lower threshold is used for additional (pyramiding) entries once the main trend has already been identified. For instance, if set to 0.5, the script looks for acceleration crossing ±0.5 times its average acceleration to add extra positions.
5. Max Pyramiding Entries (maxPyramidingEntries)
This sets a limit on how many extra positions can be opened (beyond the first main signal) in a single directional trend. The default of 3 ensures traders do not become overexposed.
6. Show Acceleration Table (showTable)
When enabled, a small table displaying the current acceleration and its average is added to the top-right corner of the chart. This table helps monitor real-time momentum changes.
7. Smart Pyramiding (enablePyramiding)
This toggle decides whether additional entries (buy or sell) will be generated once a main signal is active. If enabled, these extra signals act as filtered entries, only firing when acceleration re-crosses a smaller threshold (pyramidingThreshold). These signals have a '+' next to their signal on the label.
8. Select Color Scheme (selectedColorScheme)
Allows choosing between various pre-coded color themes, such as Default, Emerald, Sapphire, Golden Blaze, Mystic, Monochrome, Pastel, Vibrant, Earth, or Neon. Each theme applies a distinct pair of colors for bullish and bearish conditions.
9. Trend Filter (TrendFilter)
Lets the user pick one of several moving average approaches to determine the prevailing trend. The options include:
Short Term (TEMA)
EWMA
Medium Term (HMA)
Classic (SMA)
Quick Reaction (DEMA)
Each method behaves differently, balancing reactivity and smoothness.
10. Slope Lookback (slopeOffset)
Used to measure the slope of the trend filter over a set number of bars (default is 10). This slope then influences the coloring of the trend filter line, indicating bullish or bearish tilt.
Note: The script refers to this as the "Massive Slope Index," but it effectively serves as a Trend Slope Calculation, measuring how the chosen trend filter changes over a specified period.
11. Alerts for Buy/Sell and Pyramiding Signals
The script includes built-in alert conditions that can be enabled or configured. These alerts trigger whenever the script detects a main Buy or Sell signal, as well as extra (pyramiding) signals if Smart Pyramiding is active. This feature allows traders to receive immediate notifications or automate a trading response.
Calculation Methodology
1. Velocity and Acceleration
Velocity is derived by subtracting the closing price from its value length bars ago. Acceleration is the difference in velocity over an additional length period. This highlights how quickly momentum is shifting.
2. Average Acceleration
The script smooths raw acceleration with a simple moving average (SMA) using the smoothing input. Comparing current acceleration against this average provides a threshold-based signal mechanism.
3. Trend Filter
Users can pick one of five moving average types to form a trend baseline. These range from quick-reacting methods (DEMA, TEMA) to smoother options (SMA, HMA, EWMA). The script checks whether the price is above or below this filter to confirm trend direction.
4. Buy/Sell Logic
A buy occurs when acceleration surpasses avgAcceleration * threshold and price closes above the trend filter. A sell occurs under the opposite conditions. An additional overbought/oversold check (based on a longer SMA) refines these signals further.
When price is considered oversold (i.e., close is below a longer-term SMA), a bullish acceleration signal has a higher likelihood of success because it indicates that the market is attempting to reverse from a lower price region. Conversely, when price is considered overbought (close is above this longer-term SMA), a bearish acceleration signal is more likely to be valid. This helps reduce false signals by waiting until the market is extended enough that a reversal or continuation has a stronger chance of following through.
5. Smart Pyramiding
Once a main buy or sell signal is triggered, additional (filtered) entries can be taken if acceleration crosses a smaller multiplier (pyramidingThreshold). This helps traders scale into strong moves. The script enforces a cap (maxPyramidingEntries) to limit risk.
6. Visual Elements
Candles can be recolored based on the active signal. Labels appear on the chart whenever a main or pyramiding entry signal is triggered. An optional table can show real-time acceleration values.
Color Schemes
The script includes a variety of predefined color themes. For bullish conditions, it might use turquoise or green, and for bearish conditions, magenta or red—depending on which color scheme the user selects. Each scheme aims to provide clear visual differentiation between bullish and bearish market states.
Why Each Indicator Was Part of This Component
Acceleration is employed to detect swift changes in momentum, capturing shifts that may not yet appear in more traditional measures. To further adapt to different trading styles and market conditions, several moving average methods are incorporated:
• TEMA (Triple Exponential Moving Average) is chosen for its ability to reduce lag more effectively than a standard EMA while still reacting swiftly to price changes. Its construction layers exponential smoothing in a way that can highlight sudden momentum shifts without sacrificing too much smoothness.
• DEMA (Double Exponential Moving Average) provides a faster response than a single EMA by using two layers of exponential smoothing. It is slightly less smoothed than TEMA but can alert traders to momentum changes earlier, though with a higher risk of noise in choppier markets.
• HMA (Hull Moving Average) is known for its balance of smoothness and reduced lag. Its weighted calculations help track trend direction clearly, making it useful for traders who want a smoother line that still reacts fairly quickly.
• SMA (Simple Moving Average) is the classic baseline for smoothing price data. It offers a clear, stable perspective on long-term trends, though it reacts more slowly than other methods. Its simplicity can be beneficial in lower-volatility or more stable market environments.
• EWMA (Exponentially Weighted Moving Average) provides a middle ground by emphasizing recent price data while still retaining some degree of smoothing. It typically responds faster than an SMA but is less aggressive than DEMA or TEMA.
Alongside these moving average techniques, the script employs a slope calculation (referred to as the “Massive Slope Index”) to visually indicate whether the chosen filter is sloping upward or downward. This adds an extra layer of clarity to directional analysis. The indicator also uses overbought/oversold checks, based on a longer-term SMA, to help filter out signals in overstretched markets—reducing the likelihood of false entries in conditions where the price is already extensively extended.
Additional Features
Alerts can be set up for both main signals and additional pyramiding signals, which is helpful for automated or semi-automated trading. The optional acceleration table offers quick reference values, making momentum monitoring more intuitive. Including explicit alert conditions for Buy/Sell and Pyramiding ensures traders can respond promptly to market movements or integrate these triggers into automated strategies.
Summary
This script serves as a comprehensive momentum-based trading framework, leveraging acceleration metrics and multiple moving average filters to identify potential shifts in market direction. By combining overbought/oversold checks with threshold-based triggers, it aims to reduce the noise that commonly plagues purely reactive indicators. The flexibility of Smart Pyramiding, customizable color schemes, and built-in alerts allows users to tailor their experience and respond swiftly to valid signals, potentially enhancing trading decisions across various market conditions.
Disclaimer
All trading involves significant risk, and users should apply their own judgment, risk management, and broader analysis before making investment decisions.
Volume Predictor [PhenLabs]📊 Volume Predictor
Version: PineScript™ v6
📌 Description
The Volume Predictor is an advanced technical indicator that leverages machine learning and statistical modeling techniques to forecast future trading volume. This innovative tool analyzes historical volume patterns to predict volume levels for upcoming bars, providing traders with valuable insights into potential market activity. By combining multiple prediction algorithms with pattern recognition techniques, the indicator delivers forward-looking volume projections that can enhance trading strategies and market analysis.
🚀 Points of Innovation:
Machine learning pattern recognition using Lorentzian distance metrics
Multi-algorithm prediction framework with algorithm selection
Ensemble learning approach combining multiple prediction methods
Real-time accuracy metrics with visual performance dashboard
Dynamic volume normalization for consistent scale representation
Forward-looking visualization with configurable prediction horizon
🔧 Core Components
Pattern Recognition Engine : Identifies similar historical volume patterns using Lorentzian distance metrics
Multi-Algorithm Framework : Offers five distinct prediction methods with configurable parameters
Volume Normalization : Converts raw volume to percentage scale for consistent analysis
Accuracy Tracking : Continuously evaluates prediction performance against actual outcomes
Advanced Visualization : Displays actual vs. predicted volume with configurable future bar projections
Interactive Dashboard : Shows real-time performance metrics and prediction accuracy
🔥 Key Features
The indicator provides comprehensive volume analysis through:
Multiple Prediction Methods : Choose from Lorentzian, KNN Pattern, Ensemble, EMA, or Linear Regression algorithms
Pattern Matching : Identifies similar historical volume patterns to project future volume
Adaptive Predictions : Generates volume forecasts for multiple bars into the future
Performance Tracking : Calculates and displays real-time prediction accuracy metrics
Normalized Scale : Presents volume as a percentage of historical maximums for consistent analysis
Customizable Visualization : Configure how predictions and actual volumes are displayed
Interactive Dashboard : View algorithm performance metrics in a customizable information panel
🎨 Visualization
Actual Volume Columns : Color-coded green/red bars showing current normalized volume
Prediction Columns : Semi-transparent blue columns representing predicted volume levels
Future Bar Projections : Forward-looking volume predictions with configurable transparency
Prediction Dots : Optional white dots highlighting future prediction points
Reference Lines : Visual guides showing the normalized volume scale
Performance Dashboard : Customizable panel displaying prediction method and accuracy metrics
📖 Usage Guidelines
History Lookback Period
Default: 20
Range: 5-100
This setting determines how many historical bars are analyzed for pattern matching. A longer period provides more historical data for pattern recognition but may reduce responsiveness to recent changes. A shorter period emphasizes recent market behavior but might miss longer-term patterns.
🧠 Prediction Method
Algorithm
Default: Lorentzian
Options: Lorentzian, KNN Pattern, Ensemble, EMA, Linear Regression
Selects the algorithm used for volume prediction:
Lorentzian: Uses Lorentzian distance metrics for pattern recognition, offering excellent noise resistance
KNN Pattern: Traditional K-Nearest Neighbors approach for historical pattern matching
Ensemble: Combines multiple methods with weighted averaging for robust predictions
EMA: Simple exponential moving average projection for trend-following predictions
Linear Regression: Projects future values based on linear trend analysis
Pattern Length
Default: 5
Range: 3-10
Defines the number of bars in each pattern for machine learning methods. Shorter patterns increase sensitivity to recent changes, while longer patterns may identify more complex structures but require more historical data.
Neighbors Count
Default: 3
Range: 1-5
Sets the K value (number of nearest neighbors) used in KNN and Lorentzian methods. Higher values produce smoother predictions by averaging more historical patterns, while lower values may capture more specific patterns but could be more susceptible to noise.
Prediction Horizon
Default: 5
Range: 1-10
Determines how many future bars to predict. Longer horizons provide more forward-looking information but typically decrease accuracy as the prediction window extends.
📊 Display Settings
Display Mode
Default: Overlay
Options: Overlay, Prediction Only
Controls how volume information is displayed:
Overlay: Shows both actual volume and predictions on the same chart
Prediction Only: Displays only the predictions without actual volume
Show Prediction Dots
Default: false
When enabled, adds white dots to future predictions for improved visibility and clarity.
Future Bar Transparency (%)
Default: 70
Range: 0-90
Controls the transparency of future prediction bars. Higher values make future bars more transparent, while lower values make them more visible.
📱 Dashboard Settings
Show Dashboard
Default: true
Toggles display of the prediction accuracy dashboard. When enabled, shows real-time accuracy metrics.
Dashboard Location
Default: Bottom Right
Options: Top Left, Top Right, Bottom Left, Bottom Right
Determines where the dashboard appears on the chart.
Dashboard Text Size
Default: Normal
Options: Small, Normal, Large
Controls the size of text in the dashboard for various display sizes.
Dashboard Style
Default: Solid
Options: Solid, Transparent
Sets the visual style of the dashboard background.
Understanding Accuracy Metrics
The dashboard provides key performance metrics to evaluate prediction quality:
Average Error
Shows the average difference between predicted and actual values
Positive values indicate the prediction tends to be higher than actual volume
Negative values indicate the prediction tends to be lower than actual volume
Values closer to zero indicate better prediction accuracy
Accuracy Percentage
A measure of how close predictions are to actual outcomes
Higher percentages (>70%) indicate excellent prediction quality
Moderate percentages (50-70%) indicate acceptable predictions
Lower percentages (<50%) suggest weaker prediction reliability
The accuracy metrics are color-coded for quick assessment:
Green: Strong prediction performance
Orange: Moderate prediction performance
Red: Weaker prediction performance
✅ Best Use Cases
Anticipate upcoming volume spikes or drops
Identify potential volume divergences from price action
Plan entries and exits around expected volume changes
Filter trading signals based on predicted volume support
Optimize position sizing by forecasting market participation
Prepare for potential volatility changes signaled by volume predictions
Enhance technical pattern analysis with volume projection context
⚠️ Limitations
Volume predictions become less accurate over longer time horizons
Performance varies based on market conditions and asset characteristics
Works best on liquid assets with consistent volume patterns
Requires sufficient historical data for pattern recognition
Sudden market events can disrupt prediction accuracy
Volume spikes may be muted in predictions due to normalization
💡 What Makes This Unique
Machine Learning Approach : Applies Lorentzian distance metrics for robust pattern matching
Algorithm Selection : Offers multiple prediction methods to suit different market conditions
Real-time Accuracy Tracking : Provides continuous feedback on prediction performance
Forward Projection : Visualizes multiple future bars with configurable display options
Normalized Scale : Presents volume as a percentage of maximum volume for consistent analysis
Interactive Dashboard : Displays key metrics with customizable appearance and placement
🔬 How It Works
The Volume Predictor processes market data through five main steps:
1. Volume Normalization:
Converts raw volume to percentage of maximum volume in lookback period
Creates consistent scale representation across different timeframes and assets
Stores historical normalized volumes for pattern analysis
2. Pattern Detection:
Identifies similar volume patterns in historical data
Uses Lorentzian distance metrics for robust similarity measurement
Determines strength of pattern match for prediction weighting
3. Algorithm Processing:
Applies selected prediction algorithm to historical patterns
For KNN/Lorentzian: Finds K nearest neighbors and calculates weighted prediction
For Ensemble: Combines multiple methods with optimized weighting
For EMA/Linear Regression: Projects trends based on statistical models
4. Accuracy Calculation:
Compares previous predictions to actual outcomes
Calculates average error and prediction accuracy
Updates performance metrics in real-time
5. Visualization:
Displays normalized actual volume with color-coding
Shows current and future volume predictions
Presents performance metrics through interactive dashboard
💡 Note:
The Volume Predictor performs optimally on liquid assets with established volume patterns. It’s most effective when used in conjunction with price action analysis and other technical indicators. The multi-algorithm approach allows adaptation to different market conditions by switching prediction methods. Pay special attention to the accuracy metrics when evaluating prediction reliability, as sudden market changes can temporarily reduce prediction quality. The normalized percentage scale makes the indicator consistent across different assets and timeframes, providing a standardized approach to volume analysis.
Custom Time Alert with Vertical Line📌 Detailed Explanation of the Custom Time Alert with Vertical Line in Pine Script v5
This script is a time-based alert system designed for TradingView. It allows traders to set a specific hour and minute for alerts and provides visual indicators on the chart, including a marker when the alert triggers and a vertical line at the alert time.
🔹 Main Features
Custom Alert Time → Users can specify the exact hour and minute for an alert.
Time Zone Offset Support → Users can manually adjust their local UTC offset to ensure alerts trigger at the correct time.
Real-Time Alert Condition → When the market reaches the set time, an alert notification is triggered.
Chart Visualization → A red marker appears when the alert is activated, and a blue vertical line is drawn at the alert time.
Automated Calculation → The script adjusts the alert time based on the user’s time zone settings.
🛠️ How It Works
User Input for Alert Time
The script allows users to enter their desired alert hour (0-23) and minute (0-59).
This ensures the alert triggers at the exact specified time.
Time Zone Offset Handling
Users enter their UTC offset (e.g., New York is -5, Tokyo is +9).
This ensures alerts work correctly regardless of the user’s location.
Time Calculation
The script adjusts the TradingView time by adding the time zone offset in milliseconds.
This converts the UTC-based TradingView time into the user’s local time.
Checking for a Time Match
The script constantly checks if the current hour and minute match the user-defined alert time.
If they match, the script activates an alert.
Triggering Alerts
The script uses TradingView’s alertcondition() function to create an alert.
When the time matches, TradingView sends a notification (e.g., pop-up, sound, or mobile alert).
Chart Markers for Visual Alerts
A red marker is displayed on the chart when the alert triggers.
A blue vertical line is drawn at the exact alert time.
📌 Example Use Cases
📈 1. Forex Traders Monitoring Market Opens
A forex trader who trades the London session wants an alert when the market opens at 8:00 AM UTC.
The trader sets:
Alert Hour = 8
Alert Minute = 0
Time Zone Offset = 0 (for UTC)
When the market reaches 8:00 AM UTC, the script triggers an alert.
📈 2. Stock Market Open Alerts
A trader in New York (EST) wants an alert at 9:30 AM Eastern Time (New York Stock Exchange open).
New York’s UTC offset is -5.
The trader sets:
Alert Hour = 9
Alert Minute = 30
Time Zone Offset = -5
The script ensures the alert triggers at 9:30 AM EST.
📈 3. Crypto Trader Watching a Specific Time
A crypto trader wants an alert for a specific strategy at 3:00 PM in Tokyo (UTC+9).
Tokyo’s UTC offset is +9.
The trader sets:
Alert Hour = 15
Alert Minute = 0
Time Zone Offset = +9
The script ensures the alert triggers exactly at 3:00 PM Tokyo time.
ADX + DMI (HMA Version)📝 Description (What This Indicator Does)
🚀 ADX + DMI (HMA Version) is a trend strength oscillator that enhances the traditional ADX by using the Hull Moving Average (HMA) instead of EMA.
✅ This results in a much faster and more responsive trend detection while filtering out choppy price action.
🎯 What This Indicator Does:
1️⃣ Measures Trend Strength – ADX shows when a trend is strong or weak.
2️⃣ Identifies Trend Direction – DI+ (Green) shows bullish momentum, DI- (Red) shows bearish momentum.
3️⃣ Uses Hull Moving Average (HMA) for Faster Signals – Removes lag and reacts faster to trend changes.
4️⃣ Reduces False Signals – Traditional ADX lags behind, but this version reacts quickly to reversals.
5️⃣ Good for Scalping & Day Trading – Especially for BTC 5-min and lower timeframes.
⚙ Indicator Inputs (Customization)
Input Name Example Value Purpose
ADX Length 14 Defines the smoothing for the ADX value.
DI Length 14 Defines how DI+ and DI- are calculated.
HMA Length 24 Hull Moving Average smoothing for ADX & DI+.
Trend Threshold 25 The level above which ADX confirms a strong trend.
📌 You can adjust these settings to optimize for different assets and timeframes.
🎯 Trading Rules & How to Use It
✅ How to Identify a Strong Trend:
When ADX (Blue Line) is above 25→ A strong trend is in play.
When ADX is below 25 → The market is choppy or ranging.
✅ How to Use DI+ and DI- for Trend Direction:
If DI+ (Green) is above DI- (Red), the market is in an uptrend.
If DI- (Red) is above DI+ (Green), the market is in a downtrend.
✅ How to Confirm Entries & Exits:
1️⃣ Enter Long when DI+ crosses above DI- while ADX is rising above 25.
2️⃣ Enter Short when DI- crosses above DI+ while ADX is rising above 25.
3️⃣ Avoid trading when ADX is below 25 – the market is in a choppy range.
This should not be used as a stand alone oscillator. Trading takes skill and is risky. Use at your own risk.
This is not advise on how to trade, these are just examples of how I use the oscillator. Trade at your own risk.
You can put this on your chart versus the tradingview adx and you can adjust the settings to see the difference. This was optimized for btc on the 5 min chart. You can adjust for your trading strategy.
Fair Value Gap Finder [Find Better Trades]Fair Value Gap Finder (FVG) – Spot Institutional Imbalances
📈 Identify Key Market Imbalances
The Fair Value Gap Finder automatically detects price inefficiencies where aggressive buying or selling has created an imbalance in liquidity. These gaps, often left by institutional traders, can serve as key areas for price to revisit before continuing its trend.
🔍 How It Works:
Highlights bullish Fair Value Gaps (FVGs) in green, signaling potential support zones.
Highlights bearish Fair Value Gaps (FVGs) in red, signaling potential resistance zones.
Uses ATR-based filtering to eliminate small, insignificant gaps, focusing only on high-probability setups.
Alerts included! Get notified when a valid Fair Value Gap is detected.
📊 How to Trade Using FVGs:
✅ For Buy Trades: Wait for price to return to a bullish FVG and confirm support before entering long.
✅ For Sell Trades: Wait for price to revisit a bearish FVG and confirm resistance before entering short.
✅ Use with candlestick patterns, trend analysis, or volume for additional confirmation.
⚙️ Customizable Settings:
Adjust the ATR Multiplier to control how large a gap must be before triggering a signal.
Enable alerts to stay informed in real time when new FVGs appear.
💡 Why Use This Indicator?
Fair Value Gaps are widely used by professional traders to spot areas of liquidity, making them valuable for scalping, swing trading, and institutional-style trading.
🚀 Add it to your TradingView chart and start trading with precision!
Multi-Timeframe RPM Gauges with Custom Timeframes by DiGetIntroducing the **Multi-Timeframe RPM Gauges with Custom Timeframes + RSI Combos (mod) by DiGet** – a cutting-edge TradingView indicator meticulously crafted to revolutionize your market analysis.
Imagine having a dynamic dashboard right on your chart that consolidates the power of nine essential technical indicators—RSI, CCI, Stochastic, Williams %R, EMA crossover, Bollinger Bands, ATR, MACD, and Ichimoku Cloud—across multiple timeframes. This indicator not only displays each indicator’s score through an intuitive gauge system but also computes a combined metric to provide you with an at-a-glance understanding of market momentum and potential trend shifts.
**Key Features:**
- **Multi-Timeframe Insight:**
Configure up to four custom timeframes (e.g., 1, 5, 15, 60 minutes) to capture both short-term fluctuations and long-term trends, ensuring you never miss critical market moves.
- **Comprehensive Signal Suite:**
Benefit from a harmonious blend of signals. Whether you rely on momentum indicators like RSI and CCI, volatility measures like Bollinger Bands and ATR, or trend confirmations via EMA, MACD, and Ichimoku, every metric is normalized into actionable percentages.
- **Dynamic, Color-Coded Gauge Display:**
A built-in table presents all your data in a clear, color-coded format—green for bullish, red for bearish, and gray for neutral conditions. This visual representation allows you to quickly gauge market sentiment without sifting through complex charts.
- **Customizable Layout:**
Tailor your experience by toggling individual table columns. Whether you want to focus solely on RSI or dive deep into combined metrics like RSI & CCI or RSI & MACD, the choice is yours.
- **Optimized Utility Functions:**
Proprietary functions standardize indicator values into percentage scores, making it simpler than ever to compare different signals and spot opportunities in real time.
- **User-Friendly Interface:**
Designed for both beginners and seasoned traders, the straightforward input settings let you easily adjust technical parameters and timeframes to suit your personal trading strategy.
This indicator is not just a tool—it’s your new trading companion. It equips you with a multi-dimensional view of the market, enabling faster, more informed decision-making. Whether you’re scanning across various assets or drilling down on a single chart, the Multi-Timeframe RPM Gauges empower you to interpret market data with unprecedented clarity.
Add this indicator to your TradingView chart today and experience a smarter, more efficient way to navigate the markets. Join the community of traders who have elevated their analysis—and be ready to receive countless thanks as you transform your trading strategy!
Recency-Weighted Market Memory w/ Quantile-Based DriftRecency-Weighted Market Memory w/ Quantile-Based Drift
This indicator combines market memory, recency-weighted drift, quantile-based volatility analysis, momentum (RoC) filtering, and historical correlation checks to generate dynamic forecasts of possible future price levels. It calculates bullish and bearish forecast lines at each horizon, reflecting how the price might behave based on historical similarities.
Trading Concepts & Mathematical Foundations Explained
1) Market Memory
Concept:
Markets tend to repeat past behaviors under similar conditions. By identifying historical market states that closely match current conditions, we predict future price movements based on what happened historically.
Calculation Steps:
We select a historical lookback window (for example, 210 bars).
Each historical bar within this window is evaluated to see if its conditions match the current market. Conditions include:
Correlation between price change and bullish/bearish volume changes (over a user-defined correlation lookback period).
Momentum (Rate of Change, RoC) measured over a separate lookback period.
Only bars closely matching current conditions (within user-defined tolerance percentages) are included.
2) Recency-Weighted Drift
Concept:
Recent market movements often influence future direction. We assign more importance to recent bars to capture the current market bias effectively.
Calculation Steps:
Consider recent price changes between opens and closes for a user-defined drift lookback (for example, last 20 bars).
Give higher weight to recent bars (the most recent bar gets the highest weight, and weights decrease progressively for older bars).
Average these weighted changes separately for upward and downward movements, then combine these averages to calculate a final drift percentage relative to the current price.
3) Correlation Filtering
Concept:
Price changes often correlate strongly with bullish or bearish volume activity. By using historical correlation comparisons, we focus only on past market states with similar volume-price dynamics.
Calculation Steps:
Compute current correlations between price changes and bullish/bearish volume over the user-defined correlation lookback.
Evaluate each historical bar to see if its correlation closely matches the current correlation (within a user-specified percentage tolerance).
Only historical bars meeting this correlation criterion are selected.
4) Momentum (RoC) Filtering
Concept:
Two market periods may exhibit similar correlation structures but differ in how fast prices move (momentum). To ensure true similarity, momentum is checked as an additional filter.
Calculation Steps:
Compute the current Rate of Change (RoC) over the specified RoC lookback.
For each candidate historical bar, calculate its historical RoC.
Only include historical bars whose RoC closely matches the current RoC (within the RoC percentage tolerance).
5) Quantile-Based Volatility and Drift Amplification
Concept:
Quantiles (such as the 95th, 50th, and 5th percentiles) help gauge if current prices are near historical extremes or the median. Quantile bands measure volatility expansions and contractions.
Calculation Steps:
Calculate the 95%, 50%, and 5% quantiles of price over the quantile lookback period.
Add and subtract multiples of the standard deviation to these quantiles, creating upper and lower bands.
Measure the bands' widths relative to the current price as volatility indicators.
Determine the active quantile (95%, 50%, or 5%) based on proximity to the current price (within a percentage tolerance).
Compute the rate of change (RoC) of the active quantile to detect directional bias.
Combine volatility and quantile RoC into a scaling factor that amplifies or dampens expected price moves.
6) Expected Value (EV) Computation & Forecast Lines
Concept:
We forecast future prices based on how similarly-conditioned historical periods performed. We average historical moves to estimate the expected future price.
Calculation Steps:
For each forecast horizon (e.g., 1 to 27 bars ahead), collect all historical price moves that passed correlation and RoC filters.
Calculate average historical moves for bullish and bearish cases separately.
Adjust these averages by applying recency-weighted drift and quantile-based scaling.
Translate adjusted percentages into absolute future price forecasts.
Draw bullish and bearish forecast lines accordingly.
Indicator Inputs & Their Roles
Correlation Tolerance (%)
Adjusts how strictly the indicator matches historical correlation. Higher tolerance includes more matches, lower tolerance selects fewer but closer matches.
Price RoC Lookback and Price RoC Tolerance (%)
Controls how momentum (speed of price moves) is matched historically. Increasing tolerance broadens historical matches.
Drift Lookback (bars)
Determines the number of recent bars influencing current drift estimation.
Quantile Lookback Period and Std Dev Multipliers
Defines quantile calculation and the size of the volatility bands.
Quantile Contact Tolerance (%)
Sets how close the current price must be to a quantile for it to be considered "active."
Forecast Horizons
Specifies how many future bars to forecast.
Continuous Forecast Lines
Toggles between drawing continuous lines or separate horizontal segments for each forecast horizon.
Practical Trading Applications
Bullish & Bearish EV Lines
These forecast lines indicate expected price levels based on historical similarity. Green indicates positive expectations; red indicates negative.
Momentum vs. Mean Reversion
Wide quantile bands and high drift suggest momentum, while extremes may signal possible reversals.
Volatility Sensitivity
Forecasts adapt dynamically to market volatility. Broader bands increase forecasted price movements.
Filtering Non-Relevant Historical Data
By using both correlation and RoC filtering, irrelevant past periods are excluded, enhancing forecast reliability.
Multi-Timeframe Suitability
Adaptable parameters make this indicator suitable for different trading styles and timeframes.
Complementary Tool
This indicator provides probabilistic projections rather than direct buy or sell signals. Combine it with other trading signals and analyses for optimal results.
Important Considerations
While historically-informed forecasts are valuable, market behavior can evolve unpredictably. Always manage risks and use supplementary analysis.
Experiment extensively with input settings for your specific market and timeframe to optimize forecasting performance.
Summary
The Recency-Weighted Market Memory w/ Quantile-Based Drift indicator uniquely merges multiple sophisticated concepts, delivering dynamic, historically-informed price forecasts. By combining historical similarity, adaptive drift, momentum filtering, and quantile-driven volatility scaling, traders gain an insightful perspective on future price possibilities.
Feel free to experiment, explore, and enjoy this powerful addition to your trading toolkit!
High Volatility and Big Price Change ScannerThis Pine Script scans for high volatility and significant price changes on the chart. It uses Average True Range (ATR) to measure volatility and calculates the percentage change in price over a specified lookback period. When both conditions—high volatility (ATR above a threshold) and a significant price change (greater than the set percentage threshold)—are met, a signal is plotted below the bar. Additionally, an alert condition is included for notifications when these conditions are satisfied.
This script is useful for identifying stocks with large price movements and increased volatility, which may indicate potential trading opportunities.
VWAP with ADX Buy/Sell Signals and 50 MA BackgroundThis Pine Script combines several technical indicators to create a comprehensive chart with buy and sell signals based on the ADX and VWAP, as well as background color changes depending on the price relative to the 50-period simple moving average (SMA). Here's a breakdown of what each part of the code does:
1. VWAP Settings
Anchor Period: You can select different periods such as "Session", "Week", "Month", etc. to define the anchor period for the VWAP.
Source: The source for VWAP is set to the typical price (hlc3).
Offset: Allows for shifting the VWAP by a specified amount.
2. ADX Settings
ADX Length: The period used to calculate the ADX.
ADX Smoothing: Used to smooth the ADX for better clarity.
ADX Threshold: Used to filter out weak trends (i.e., signals when ADX > 20).
3. ADX and VWAP Calculation
The ADX values are calculated using ta.dmi(), which returns the +DI, -DI, and ADX lines.
VWAP is calculated using ta.vwap(), based on the selected price source.
4. Buy/Sell Conditions
Buy Signal: A buy signal is generated when:
The +DI crosses above the -DI (indicating an uptrend).
The ADX is above 20 (indicating a strong trend).
The closing price is above the VWAP (indicating bullish market sentiment).
Sell Signal: A sell signal occurs when:
The -DI crosses above the +DI (indicating a downtrend).
The ADX is above 20 (indicating a strong trend).
The closing price is below the VWAP (indicating bearish market sentiment).
5. VWAP Bands
The standard deviation of the price is calculated using ta.stdev(), and the bands are plotted at multiples of the standard deviation (1, 2, and 3).
These bands are used to highlight possible overbought or oversold conditions.
6. 50-period SMA and Background Color
The script calculates a 50-period Simple Moving Average (SMA).
The background color is then changed based on whether the price is above or below the 50-period SMA. If the price is above the SMA, the background is green (bullish), and if it’s below, it’s red (bearish).
7. Plots
The script includes plots for the VWAP line, the ADX and DI lines (optional), and the upper and lower bands.
The buy and sell signals are plotted as shapes with text labels ("BUY" and "SELL") that appear below or above the price bars.
Final Notes:
Band Plots: Three levels of bands (green, olive, teal) are plotted using standard deviation multipliers (1, 2, and 3 times the standard deviation).
Background Color: The background color changes depending on whether the price is above or below the 50 SMA, giving a visual cue for bullish or bearish market conditions.
This indicator aims to offer a multi-faceted view of the market with trend-following signals (via ADX), VWAP for intraday support/resistance, and background coloring to indicate the current trend strength based on the 50 SMA.
ATR Impact CandlesATR Impact Candles: Simplify Your Trading with Pure Price Action
You don’t need dozens of cluttered indicators to catch what really matters. With ATR Impact Candles, you get a powerful, single-tool solution that cuts through the noise by focusing on what truly drives the market: price action and volatility. This indicator highlights only those candlesticks that pack a punch—showing you when the market’s range is exceptionally strong relative to its recent behavior. Whether you’re a scalper or a swing trader, ATR Impact Candles empowers you to time your entries and exits with confidence, letting you trade based on real market momentum.
⸻
Indicator Overview
The indicator is designed for TradingView and is implemented in Pine Script (version 5). Its primary purpose is to highlight specific candles that meet a defined volatility condition based on the Average True Range (ATR). Instead of modifying every candle’s appearance, the indicator only changes the color of those “signal” candles that exceed a user-defined multiple of the ATR. The rest of the candles remain in their traditional black and white appearance—preserving the classic candlestick chart look.
⸻
Key Features
1. ATR-Based Signal Identification:
• ATR Calculation:
The indicator calculates the ATR using a configurable lookback period (default is 14 periods). The ATR is a common volatility measure that reflects the average range of price movement.
• Threshold Condition:
A candle is flagged as a signal if its range (high minus low) meets or exceeds a specified multiple (the “ATR Factor”) of the ATR. By default, this factor is set to 2, meaning any candle whose range is at least twice the ATR is considered significant.
2. Dynamic Candle Coloring:
• Signal Candles:
• When a candle meets the ATR threshold condition:
• Up Candles: are colored green.
• Down Candles: are colored red.
• Non-Signal Candles:
• Candles that do not meet the threshold condition retain their classic appearance:
• Up candles are white.
• Down candles are black.
3. User Configurability:
• ATR Period:
Traders can adjust the ATR period to tailor the volatility measure to different markets or timeframes.
• ATR Factor:
The multiple of the ATR that defines a signal candle is also configurable, giving flexibility to experiment with different thresholds for what constitutes “significant” price movement.
• Overlay Display:
The indicator runs in overlay mode on the chart, meaning it directly affects the appearance of the candlestick bars without interfering with other chart elements.
4. Additional Visual Aid:
• Threshold Line Plot:
The script optionally plots a line representing the ATR multiplied by the chosen factor. This line serves as a visual benchmark on the chart, allowing traders to see at what level the ATR threshold lies relative to the price action.
⸻
How It Works
1. ATR Calculation:
The indicator first calculates the Average True Range (ATR) for the defined period. This value is updated for each new candle.
2. Range Comparison:
For each candle, the indicator calculates the range (high - low) and compares it to the threshold, which is the ATR multiplied by the user-defined factor.
3. Conditional Coloring:
• If the Candle’s Range ≥ (ATR * Factor):
• The candle is marked as a “signal candle.”
• Its color is set to green if it is an up candle (close is greater than or equal to open) or red if it is a down candle.
• Otherwise:
• The candle retains its classic look, with up candles in white and down candles in black.
4. Chart Display:
By applying these rules to every candle, the indicator visually emphasizes those moments when the market shows unusually large price movements relative to its recent average volatility. This helps traders quickly spot potential breakouts or reversals.
⸻
Practical Applications
• Volatility Breakouts:
Identify candles that may signal the start of a breakout or strong reversal.
• Risk Management:
Adjust stop-loss levels or position sizes when unusually volatile candles are detected.
• Signal Confirmation:
Combine with other technical indicators or chart patterns to reinforce entry or exit decisions.
⸻
ATR Impact Candles is your essential, no-nonsense tool for filtering out market noise and focusing solely on significant price action. Simplify your trading decisions and harness the power of volatility with one clear, effective indicator.
ROC + SMI Auto Adjust
This indicator combines the Rate of Change (ROC) and the Stochastic Momentum Index (SMI) with automatically adjusted parameters for different time frames (short, medium, long). It normalizes the ROC to match the SMI levels, displays the ROC as a histogram and the SMI as lines, highlights overbought/oversold zones and includes a settings table. Ideal for analyzing momentum on different time frames.
Key Features:
Automatic Parameter Adjustment:
The script detects the current chart time frame (e.g. 1-minute, 1-hour, daily) and adjusts the parameters for the ROC and SMI accordingly.
Parameters such as ROC length, SMI length and smoothing periods are optimized for short, medium and long term time frames.
Rate of Change (ROC):
ROC measures the percentage change in price over a specified period.
The script normalizes the ROC values to match the SMI range, making it easier to compare the two indicators on the same scale.
The ROC is displayed as a histogram, where positive values are colored green and negative values are colored red.
Stochastic Momentum Index (SMI):
SMI is a momentum oscillator that identifies overbought and oversold conditions.
The script calculates the SMI and its signal line, plotting them on the chart.
Overbought and oversold levels are displayed as dotted lines for convenience.
SMI and SMI Signal Crossover:
When the main SMI crosses the signal line from below upwards, it may be a buy signal (bullish signal).
When the SMI crosses the signal line from above downwards, it may be a sell signal (bearish signal).
Configurable Inputs:
Users can use the automatically adjusted settings or manually override the parameters (e.g. ROC length, SMI length, smoothing periods).
Overbought and oversold levels for SMI are also configurable.
Parameter Table:
A table is displayed on the chart showing the current parameters (e.g. timeframe, ROC length, SMI length) for transparency and debugging.
The position of the table is configurable (e.g. top left, bottom right).
How it works:
The script first detects the chart timeframe and classifies it as short-term (e.g. 1M, 5M), medium-term (e.g. 1H, 4H) or long-term (e.g. D1, W1).
Based on the timeframe, it sets default values for the ROC and SMI parameters.
ROC and SMI are calculated and normalized so that they can be compared on the same scale.
ROC is displayed as a histogram, while SMI and its signal line are displayed as lines.
Overbought and oversold levels are displayed as horizontal lines.
Use cases:
Trend identification: ROC helps to identify the strength of the trend, while SMI indicates overbought/oversold conditions.
Momentum analysis: The combination of ROC and SMI provides insight into both price momentum and potential reversals.
Time frame flexibility: The auto-adjustment feature makes the script suitable for scalping (short-term), swing trading (medium-term) and long-term investing.
Liquidity Zones [ActiveQuants]The Liquidity Zones indicator detects price areas where high trading volume coincides with below-average volatility , critical zones where large players often accumulate or distribute positions. Ideal for spotting potential reversal points and strategic liquidity pools.
Core Detection Formula
Liquidity Zone = (Volume > SMA(Volume, Length) × Multiplier) AND (Short-Term Volatility < 0.5 × Average Volatility)
Volume Surge Detection
Compares current volume to its SMA (user-defined length).
Multiplies threshold with " Volume Threshold Multiplier " parameter.
Volatility Contraction Filter
Calculates 5-bar volatility (standard deviation of closes).
Compares to average volatility over " Price Std. Dev. Length " period.
Requires short-term volatility < 50% of average.
█ KEY FEATURES
Merging Consecutive Zones
If the " Merge Consecutive Zones " option is enabled, the indicator will:
Calculate the number of consecutive bars that meet the liquidity zone criteria.
Sum the volume of these consecutive bars.
Display only the most recent label for the merged zone (previous labels in the sequence are removed).
Displays volume in either
Raw units (" Units ").
Dollar-equivalent (" Currency Value ") using closing price.
Alerts
An alert condition is built into the script. Traders can selectively enable alerts via TradingView’s alert system. Whenever a liquidity zone is detected, an alert is triggered with the message: " High-volume and low-volatility zone detected! ".
█ USER INPUTS
- Liquidity Zones Color
Sets the background color for liquidity zones.
Default: Orange (with 70 transparency).
- Volume SMA Length
Determines the number of bars over which the volume simple moving average is calculated.
Default: 20 bars.
- Volume Threshold Multiplier
Multiplies the volume SMA to establish a threshold. A bar’s volume must exceed this product to be considered high volume.
Default: 2.0.
- Price Std. Dev. Length
The period used to calculate the standard deviation of the closing prices. This is the basis for measuring average volatility.
Default: 14 bars.
- Zone Volume
A toggle to display a label with the volume value on liquidity zones.
Allows you to choose how the volume is displayed: Units (shows raw volume) or Currency Value (multiplies volume by the current closing price).
Allows you to choose the font size of the volume label.
- Merge Consecutive Zones
When enabled, volumes from consecutive liquidity zones are summed into a single total, and only the most recent label is displayed (previous labels in the sequence are removed).
Default: Enabled.
- Show Last
Specifies the number of bars back that the indicator will evaluate and plot liquidity zones.
Default: 500 bars.
- Timeframe
Analysis period.
Default: Chart.
█ CONCLUSION
The Liquidity Zones indicator is a powerful tool for traders seeking to identify key areas on the chart where liquidity is concentrated, characterized by high volume and low volatility . With customizable settings for volume analysis and volatility measurement , this indicator can be integrated into a wide range of trading strategies. It not only highlights these zones visually but also provides volume data labels and alerts for timely decision-making.
█ IMPORTANT NOTES
⚠ Volume and Volatility Settings: Adjust the Volume SMA Length , Volume Threshold Multiplier , and Price Std. Dev. Length to suit the typical trading volume and volatility of the asset you are analyzing.
⚠ Confirmed Bars Only: Signals are generated only on confirmed bars. This minimizes false signals due to intra-bar noise and also prevents indicator repainting .
⚠ Risk Management: Liquidity zones may signal areas of potential accumulation or distribution, but they should be used in conjunction with other technical analysis tools (e.g., support/resistance levels, trendlines, or momentum indicators). Trading involves risk, and it is recommended to combine this indicator with proper risk management techniques.
█ RISK DISCLAIMER
Trading involves substantial risk of loss. Liquidity zones indicate potential interest areas but don't guarantee price reactions. Always confirm with additional analysis and proper risk management. Past performance is not indicative of future results.
📈 Happy trading! 🚀
